TraumaICD Bidirectional Encoder Representation From Transformers: A Natural Language Processing Algorithm to Extract Injury International Classification of Diseases, 10th Edition Diagnosis Code From Free Text

To develop and validate TraumaICDBERT, a natural language processing algorithm to predict injury International Classification of Diseases, 10th edition (ICD-10) diagnosis codes from trauma tertiary survey notes. The adoption of ICD-10 diagnosis codes in clinical settings for injury prediction is hin...

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Vydané v:Annals of surgery Ročník 280; číslo 1; s. 150
Hlavní autori: Choi, Jeff, Chen, Yifu, Sivura, Alexander, Vendrow, Edward B, Wang, Jenny, Spain, David A
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 01.07.2024
Predmet:
Algorithms
Electronic Health Records
Humans
International Classification of Diseases
Natural Language Processing
Wounds and Injuries - classification
Wounds and Injuries - diagnosis
ISSN:1528-1140, 1528-1140
On-line prístup:Zistit podrobnosti o prístupe
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Popis
Shrnutí:To develop and validate TraumaICDBERT, a natural language processing algorithm to predict injury International Classification of Diseases, 10th edition (ICD-10) diagnosis codes from trauma tertiary survey notes. The adoption of ICD-10 diagnosis codes in clinical settings for injury prediction is hindered by the lack of real-time availability. Existing natural language processing algorithms have limitations in accurately predicting injury ICD-10 diagnosis codes. Trauma tertiary survey notes from hospital encounters of adults between January 2016 and June 2021 were used to develop and validate TraumaICD Bidirectional Encoder Representation from Transformers (TraumaICDBERT), an algorithm based on BioLinkBERT. The performance of TraumaICDBERT was compared with Amazon Web Services Comprehend Medical, an existing natural language processing tool. A data set of 3478 tertiary survey notes with 15,762 4-character injury ICD-10 diagnosis codes was analyzed. TraumaICDBERT outperformed Amazon Web Services Comprehend Medical across all evaluated metrics. On average, each tertiary survey note was associated with 3.8 (SD: 2.9) trauma registrar-extracted 4-character injury ICD-10 diagnosis codes. TraumaICDBERT demonstrates promising initial performance in predicting injury ICD-10 diagnosis codes from trauma tertiary survey notes, potentially facilitating the adoption of downstream prediction tools in clinical settings.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1528-1140
1528-1140
DOI:10.1097/SLA.0000000000006107